DATA
6de0bf9f5b
Vendored deer-flow upstream (bytedance/deer-flow) plus prompt-injection hardening: - New deerflow.security package: content_delimiter, html_cleaner, sanitizer (8 layers — invisible chars, control chars, symbols, NFC, PUA, tag chars, horizontal whitespace collapse with newline/tab preservation, length cap) - New deerflow.community.searx package: web_search, web_fetch, image_search backed by a private SearX instance, every external string sanitized and wrapped in <<<EXTERNAL_UNTRUSTED_CONTENT>>> delimiters - All native community web providers (ddg_search, tavily, exa, firecrawl, jina_ai, infoquest, image_search) replaced with hard-fail stubs that raise NativeWebToolDisabledError at import time, so a misconfigured tool.use path fails loud rather than silently falling back to unsanitized output - Native client back-doors (jina_client.py, infoquest_client.py) stubbed too - Native-tool tests quarantined under tests/_disabled_native/ (collect_ignore_glob via local conftest.py) - Sanitizer Layer 7 fix: only collapse horizontal whitespace, preserve newlines and tabs so list/table structure survives - Hardened runtime config.yaml references only the searx-backed tools - Factory overlay (backend/) kept in sync with deer-flow tree as a reference / source See HARDENING.md for the full audit trail and verification steps.
167 lines
4.9 KiB
Markdown
167 lines
4.9 KiB
Markdown
---
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name: github-deep-research
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description: Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.
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---
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# GitHub Deep Research Skill
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Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.
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## Research Workflow
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- Round 1: GitHub API
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- Round 2: Discovery
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- Round 3: Deep Investigation
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- Round 4: Deep Dive
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## Core Methodology
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### Query Strategy
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**Broad to Narrow**: Start with GitHub API, then general queries, refine based on findings.
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```
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Round 1: GitHub API
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Round 2: "{topic} overview"
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Round 3: "{topic} architecture", "{topic} vs alternatives"
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Round 4: "{topic} issues", "{topic} roadmap", "site:github.com {topic}"
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```
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**Source Prioritization**:
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1. Official docs/repos (highest weight)
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2. Technical blogs (Medium, Dev.to)
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3. News articles (verified outlets)
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4. Community discussions (Reddit, HN)
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5. Social media (lowest weight, for sentiment)
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### Research Rounds
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**Round 1 - GitHub API**
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Directly execute `scripts/github_api.py` without `read_file()`:
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```bash
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python /path/to/skill/scripts/github_api.py <owner> <repo> summary
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python /path/to/skill/scripts/github_api.py <owner> <repo> readme
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python /path/to/skill/scripts/github_api.py <owner> <repo> tree
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```
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**Available commands (the last argument of `github_api.py`):**
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- summary
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- info
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- readme
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- tree
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- languages
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- contributors
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- commits
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- issues
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- prs
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- releases
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**Round 2 - Discovery (3-5 web_search)**
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- Get overview and identify key terms
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- Find official website/repo
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- Identify main players/competitors
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**Round 3 - Deep Investigation (5-10 web_search + web_fetch)**
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- Technical architecture details
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- Timeline of key events
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- Community sentiment
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- Use web_fetch on valuable URLs for full content
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**Round 4 - Deep Dive**
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- Analyze commit history for timeline
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- Review issues/PRs for feature evolution
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- Check contributor activity
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## Report Structure
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Follow template in `assets/report_template.md`:
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1. **Metadata Block** - Date, confidence level, subject
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2. **Executive Summary** - 2-3 sentence overview with key metrics
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3. **Chronological Timeline** - Phased breakdown with dates
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4. **Key Analysis Sections** - Topic-specific deep dives
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5. **Metrics & Comparisons** - Tables, growth charts
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6. **Strengths & Weaknesses** - Balanced assessment
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7. **Sources** - Categorized references
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8. **Confidence Assessment** - Claims by confidence level
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9. **Methodology** - Research approach used
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### Mermaid Diagrams
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Include diagrams where helpful:
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**Timeline (Gantt)**:
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```mermaid
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gantt
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title Project Timeline
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dateFormat YYYY-MM-DD
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section Phase 1
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Development :2025-01-01, 2025-03-01
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section Phase 2
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Launch :2025-03-01, 2025-04-01
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```
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**Architecture (Flowchart)**:
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```mermaid
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flowchart TD
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A[User] --> B[Coordinator]
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B --> C[Planner]
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C --> D[Research Team]
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D --> E[Reporter]
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```
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**Comparison (Pie/Bar)**:
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```mermaid
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pie title Market Share
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"Project A" : 45
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"Project B" : 30
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"Others" : 25
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```
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## Confidence Scoring
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Assign confidence based on source quality:
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| Confidence | Criteria |
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|------------|----------|
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| High (90%+) | Official docs, GitHub data, multiple corroborating sources |
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| Medium (70-89%) | Single reliable source, recent articles |
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| Low (50-69%) | Social media, unverified claims, outdated info |
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## Output
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Save report as: `research_{topic}_{YYYYMMDD}.md`
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### Formatting Rules
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- Chinese content: Use full-width punctuation（，。：；！？）
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- Technical terms: Provide Wiki/doc URL on first mention
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- Tables: Use for metrics, comparisons
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- Code blocks: For technical examples
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- Mermaid: For architecture, timelines, flows
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## Best Practices
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1. **Start with official sources** - Repo, docs, company blog
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2. **Verify dates from commits/PRs** - More reliable than articles
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3. **Triangulate claims** - 2+ independent sources
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4. **Note conflicting info** - Don't hide contradictions
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5. **Distinguish fact vs opinion** - Label speculation clearly
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6. **CRITICAL: Always include inline citations** - Use `[citation:Title](URL)` format immediately after each claim from external sources
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7. **Extract URLs from search results** - web_search returns {title, url, snippet} - always use the URL field
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8. **Update as you go** - Don't wait until end to synthesize
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### Citation Examples
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**Good - With inline citations:**
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```markdown
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The project gained 10,000 stars within 3 months of launch [citation:GitHub Stats](https://github.com/owner/repo).
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The architecture uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph).
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```
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**Bad - Without citations:**
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```markdown
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The project gained 10,000 stars within 3 months of launch.
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The architecture uses LangGraph for workflow orchestration.
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```
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